Nursing Retrieval-Augmented Generation: Retrieval augmented generation for nursing question answering with large language models
Abstract
OBJECTIVE: This study aimed to develop a Nursing Retrieval-Augmented Generation (NurRAG) system based on large language models (LLMs) and to evaluate its accuracy and clinical applicability in nursing question answering.
METHODS: A multidisciplinary team consisting of nursing experts, artificial intelligence researchers, and information engineers collaboratively designed the NurRAG framework following the principles of retrieval-augmented generation. The system included four functional modules: 1) construction of a nursing knowledge base through document normalization, embedding, and vector indexing; 2) nursing question filtering using a supervised classifier; 3) semantic retrieval and re-ranking for evidence selection; and 4) evidence-conditioned language model generation to produce citation-based nursing answers. The system was securely deployed on hospital intranet servers using Docker containers. Performance evaluation was conducted with 1,000 expert-verified nursing question-answer pairs. Semantic fidelity was assessed using Recall Oriented Understudy for Gisting Evaluation - Longest Common Subsequence (ROUGE-L), and clinical correctness was measured using Accuracy.
RESULTS: The NurRAG system achieved significant improvements in both semantic fidelity and answer accuracy compared with conventional large language models. For ChatGLM2-6B, ROUGE-L increased from (30.73 ± 1.48) % to (64.27 ± 0.27) %, and accuracy increased from (49.08 ± 0.92) % to (75.83 ± 0.35) %. For…
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